

import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__snake_case				:      str   		=			get_tests_dir("""fixtures/spiece.model""")



@require_sentencepiece
@require_tokenizers
class        A__(a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				str       					=			AlbertTokenizer
						_A    :				Dict       					=			AlbertTokenizerFast
						_A    :				str       					=			True
						_A    :				int       					=			True
						_A    :				int       					=			True





						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									super().setUp()

									# We have a SentencePiece fixture for testing
									a_						:						str 							= AlbertTokenizer(_lowercase    )
									tokenizer.save_pretrained(self.tmpdirname    )





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> Any:
									a_						:						Dict 							= """this is a test"""
									a_						:						Any 							= """this is a test"""
									return input_text, output_text





						def 			UpperCamelCase__					(    self    )     -> Dict:
									a_						:						Optional[int] 							= """<pad>"""
									a_						:						Dict 							= 0

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowercase    )  ,      _lowercase    )
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowercase    )  ,      _lowercase    )





						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						List[Any] 							= list(self.get_tokenizer().get_vocab().keys()    )

									self.assertEqual(vocab_keys[0]  ,      """<pad>"""    )
									self.assertEqual(vocab_keys[1]  ,      """<unk>"""    )
									self.assertEqual(vocab_keys[-1]  ,      """▁eloquent"""    )
									self.assertEqual(len(_lowercase    )  ,      30_000    )





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									self.assertEqual(self.get_tokenizer().vocab_size  ,      30_000    )





						def 			UpperCamelCase__					(    self    )     -> Dict:
									if not self.test_rust_tokenizer:
												return

									a_						:						Tuple 							= self.get_tokenizer()
									a_						:						Union[str, Any] 							= self.get_rust_tokenizer()

									a_						:						Union[str, Any] 							= """I was born in 92000, and this is falsé."""

									a_						:						str 							= tokenizer.tokenize(_lowercase    )
									a_						:						List[str] 							= rust_tokenizer.tokenize(_lowercase    )
									self.assertListEqual(_lowercase  ,      _lowercase    )

									a_						:						Dict 							= tokenizer.encode(_lowercase  ,      add_special_tokens=_lowercase    )
									a_						:						List[Any] 							= rust_tokenizer.encode(_lowercase  ,      add_special_tokens=_lowercase    )
									self.assertListEqual(_lowercase  ,      _lowercase    )

									a_						:						str 							= self.get_rust_tokenizer()
									a_						:						Optional[Any] 							= tokenizer.encode(_lowercase    )
									a_						:						List[str] 							= rust_tokenizer.encode(_lowercase    )
									self.assertListEqual(_lowercase  ,      _lowercase    )





						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						Tuple 							= AlbertTokenizer(_lowercase  ,      keep_accents=_lowercase    )

									a_						:						Union[str, Any] 							= tokenizer.tokenize("""This is a test"""    )
									self.assertListEqual(_lowercase  ,      ["""▁this""", """▁is""", """▁a""", """▁test"""]    )

									self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase    )  ,      [48, 25, 21, 1_289]    )

									a_						:						List[str] 							= tokenizer.tokenize("""I was born in 92000, and this is falsé."""    )
									self.assertListEqual(
									    _lowercase  ,      ["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""]    )
									a_						:						Optional[int] 							= tokenizer.convert_tokens_to_ids(_lowercase    )
									self.assertListEqual(_lowercase  ,      [31, 23, 386, 19, 561, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]    )

									a_						:						Dict 							= tokenizer.convert_ids_to_tokens(_lowercase    )
									self.assertListEqual(
									    _lowercase  ,      ["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""]  ,      )





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						Union[str, Any] 							= AlbertTokenizer(_lowercase    )

									a_						:						Optional[int] 							= tokenizer.encode("""sequence builders"""    )
									a_						:						Union[str, Any] 							= tokenizer.encode("""multi-sequence build"""    )

									a_						:						int 							= tokenizer.build_inputs_with_special_tokens(_lowercase    )
									a_						:						Optional[int] 							= tokenizer.build_inputs_with_special_tokens(_lowercase  ,      _lowercase    )

									assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
									assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
									    tokenizer.sep_token_id
									]





						@slow
						def 			UpperCamelCase__					(    self    )     -> List[str]:
									# fmt: off
									a_						:						Union[str, Any] 							= {"""attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """input_ids""": [[2, 21_970, 13, 5, 6_092, 167, 28, 7_103, 2_153, 673, 8, 7_028, 12_051, 18, 17, 7_103, 2_153, 673, 8, 3_515, 18_684, 8, 4_461, 6, 1_927, 297, 8, 12_060, 2_607, 18, 13, 5, 4_461, 15, 10_538, 38, 8, 135, 15, 822, 58, 15, 993, 10_363, 15, 1_460, 8_005, 4_461, 15, 993, 255, 2_328, 9, 9, 9, 6, 26, 1_112, 816, 3_260, 13, 5, 103, 2_377, 6, 17, 1_112, 816, 2_782, 13, 5, 103, 10_641, 6, 29, 84, 2_512, 2_430, 782, 18_684, 2_761, 19, 808, 2_430, 2_556, 17, 855, 1_480, 9_477, 4_091, 128, 11_712, 15, 7_103, 2_153, 673, 17, 24_883, 9_990, 9, 3], [2, 11_502, 25, 1_006, 20, 782, 8, 11_809, 855, 1_732, 19_393, 18_667, 37, 367, 21_018, 69, 1_854, 34, 11_860, 19_124, 27, 156, 225, 17, 193, 4_141, 19, 65, 9_124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2_231, 886, 2_385, 17_659, 84, 14, 16_792, 1_952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=_lowercase  ,      model_name="""albert-base-v2"""  ,      revision="""6b6560eaf5ff2e250b00c50f380c5389a9c2d82e"""  ,      )





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case				:      int   		=			logging.get_logger(__name__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						str 							= DPTConfig(embedding_type="""hybrid""")

			if "large" in checkpoint_url:
						a_						:						List[str] 							= 1_0_2_4
						a_						:						List[str] 							= 4_0_9_6
						a_						:						Tuple 							= 2_4
						a_						:						str 							= 1_6
						a_						:						List[str] 							= [5, 1_1, 1_7, 2_3]
						a_						:						str 							= [2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
						a_						:						Any 							= (1, 3_8_4, 3_8_4)

			if "nyu" or "midas" in checkpoint_url:
						a_						:						int 							= 7_6_8
						a_						:						List[str] 							= [1, 1, 1, 0.5]
						a_						:						Tuple 							= [2_5_6, 5_1_2, 7_6_8, 7_6_8]
						a_						:						Tuple 							= 1_5_0
						a_						:						Optional[Any] 							= 1_6
						a_						:						Union[str, Any] 							= (1, 3_8_4, 3_8_4)
						a_						:						Optional[int] 							= False
						a_						:						int 							= """project"""

			if "ade" in checkpoint_url:
						a_						:						Union[str, Any] 							= True
						a_						:						int 							= 7_6_8
						a_						:						Dict 							= [1, 1, 1, 0.5]
						a_						:						Optional[int] 							= 1_5_0
						a_						:						Tuple 							= 1_6
						a_						:						str 							= """huggingface/label-files"""
						a_						:						List[Any] 							= """ade20k-id2label.json"""
						a_						:						Tuple 							= json.load(open(cached_download(hf_hub_url(a__  ,	a__  ,	repo_type="""dataset"""))  ,	"""r"""))
						a_						:						Optional[Any] 							= {int(a__): v for k, v in idalabel.items()}
						a_						:						str 							= idalabel
						a_						:						Optional[Any] 							= {v: k for k, v in idalabel.items()}
						a_						:						Optional[int] 							= [1, 1_5_0, 4_8_0, 4_8_0]

			return config, expected_shape


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Any 							= ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
			for k in ignore_keys:
						state_dict.pop(a__  ,	a__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			if (
			    "pretrained.model" in name
			    and "cls_token" not in name
			    and "pos_embed" not in name
			    and "patch_embed" not in name
			):
						a_						:						Dict 							= name.replace("""pretrained.model"""  ,	"""dpt.encoder""")
			if "pretrained.model" in name:
						a_						:						List[Any] 							= name.replace("""pretrained.model"""  ,	"""dpt.embeddings""")
			if "patch_embed" in name:
						a_						:						List[str] 							= name.replace("""patch_embed"""  ,	"""""")
			if "pos_embed" in name:
						a_						:						Tuple 							= name.replace("""pos_embed"""  ,	"""position_embeddings""")
			if "attn.proj" in name:
						a_						:						Optional[int] 							= name.replace("""attn.proj"""  ,	"""attention.output.dense""")
			if "proj" in name and "project" not in name:
						a_						:						Tuple 							= name.replace("""proj"""  ,	"""projection""")
			if "blocks" in name:
						a_						:						Any 							= name.replace("""blocks"""  ,	"""layer""")
			if "mlp.fc1" in name:
						a_						:						Tuple 							= name.replace("""mlp.fc1"""  ,	"""intermediate.dense""")
			if "mlp.fc2" in name:
						a_						:						List[str] 							= name.replace("""mlp.fc2"""  ,	"""output.dense""")
			if "norm1" in name and "backbone" not in name:
						a_						:						Dict 							= name.replace("""norm1"""  ,	"""layernorm_before""")
			if "norm2" in name and "backbone" not in name:
						a_						:						str 							= name.replace("""norm2"""  ,	"""layernorm_after""")
			if "scratch.output_conv" in name:
						a_						:						List[Any] 							= name.replace("""scratch.output_conv"""  ,	"""head""")
			if "scratch" in name:
						a_						:						Optional[int] 							= name.replace("""scratch"""  ,	"""neck""")
			if "layer1_rn" in name:
						a_						:						List[Any] 							= name.replace("""layer1_rn"""  ,	"""convs.0""")
			if "layer2_rn" in name:
						a_						:						Any 							= name.replace("""layer2_rn"""  ,	"""convs.1""")
			if "layer3_rn" in name:
						a_						:						List[Any] 							= name.replace("""layer3_rn"""  ,	"""convs.2""")
			if "layer4_rn" in name:
						a_						:						Optional[Any] 							= name.replace("""layer4_rn"""  ,	"""convs.3""")
			if "refinenet" in name:
						a_						:						Tuple 							= int(name[len("""neck.refinenet""") : len("""neck.refinenet""") + 1])
						# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
						a_						:						List[Any] 							= name.replace(f'''refinenet{layer_idx}'''  ,	f'''fusion_stage.layers.{abs(layer_idx-4)}''')
			if "out_conv" in name:
						a_						:						List[Any] 							= name.replace("""out_conv"""  ,	"""projection""")
			if "resConfUnit1" in name:
						a_						:						List[str] 							= name.replace("""resConfUnit1"""  ,	"""residual_layer1""")
			if "resConfUnit2" in name:
						a_						:						Optional[int] 							= name.replace("""resConfUnit2"""  ,	"""residual_layer2""")
			if "conv1" in name:
						a_						:						List[str] 							= name.replace("""conv1"""  ,	"""convolution1""")
			if "conv2" in name:
						a_						:						Optional[Any] 							= name.replace("""conv2"""  ,	"""convolution2""")
			# readout blocks
			if "pretrained.act_postprocess1.0.project.0" in name:
						a_						:						List[Any] 							= name.replace("""pretrained.act_postprocess1.0.project.0"""  ,	"""neck.reassemble_stage.readout_projects.0.0""")
			if "pretrained.act_postprocess2.0.project.0" in name:
						a_						:						str 							= name.replace("""pretrained.act_postprocess2.0.project.0"""  ,	"""neck.reassemble_stage.readout_projects.1.0""")
			if "pretrained.act_postprocess3.0.project.0" in name:
						a_						:						List[str] 							= name.replace("""pretrained.act_postprocess3.0.project.0"""  ,	"""neck.reassemble_stage.readout_projects.2.0""")
			if "pretrained.act_postprocess4.0.project.0" in name:
						a_						:						Any 							= name.replace("""pretrained.act_postprocess4.0.project.0"""  ,	"""neck.reassemble_stage.readout_projects.3.0""")

			# resize blocks
			if "pretrained.act_postprocess1.3" in name:
						a_						:						Dict 							= name.replace("""pretrained.act_postprocess1.3"""  ,	"""neck.reassemble_stage.layers.0.projection""")
			if "pretrained.act_postprocess1.4" in name:
						a_						:						Dict 							= name.replace("""pretrained.act_postprocess1.4"""  ,	"""neck.reassemble_stage.layers.0.resize""")
			if "pretrained.act_postprocess2.3" in name:
						a_						:						int 							= name.replace("""pretrained.act_postprocess2.3"""  ,	"""neck.reassemble_stage.layers.1.projection""")
			if "pretrained.act_postprocess2.4" in name:
						a_						:						List[Any] 							= name.replace("""pretrained.act_postprocess2.4"""  ,	"""neck.reassemble_stage.layers.1.resize""")
			if "pretrained.act_postprocess3.3" in name:
						a_						:						Union[str, Any] 							= name.replace("""pretrained.act_postprocess3.3"""  ,	"""neck.reassemble_stage.layers.2.projection""")
			if "pretrained.act_postprocess4.3" in name:
						a_						:						Dict 							= name.replace("""pretrained.act_postprocess4.3"""  ,	"""neck.reassemble_stage.layers.3.projection""")
			if "pretrained.act_postprocess4.4" in name:
						a_						:						Tuple 							= name.replace("""pretrained.act_postprocess4.4"""  ,	"""neck.reassemble_stage.layers.3.resize""")
			if "pretrained" in name:
						a_						:						List[Any] 							= name.replace("""pretrained"""  ,	"""dpt""")
			if "bn" in name:
						a_						:						Optional[int] 							= name.replace("""bn"""  ,	"""batch_norm""")
			if "head" in name:
						a_						:						Union[str, Any] 							= name.replace("""head"""  ,	"""head.head""")
			if "encoder.norm" in name:
						a_						:						Any 							= name.replace("""encoder.norm"""  ,	"""layernorm""")
			if "auxlayer" in name:
						a_						:						Any 							= name.replace("""auxlayer"""  ,	"""auxiliary_head.head""")
			if "backbone" in name:
						a_						:						Optional[Any] 							= name.replace("""backbone"""  ,	"""backbone.bit.encoder""")

			if ".." in name:
						a_						:						str 							= name.replace(""".."""  ,	""".""")

			if "stem.conv" in name:
						a_						:						Union[str, Any] 							= name.replace("""stem.conv"""  ,	"""bit.embedder.convolution""")
			if "blocks" in name:
						a_						:						Union[str, Any] 							= name.replace("""blocks"""  ,	"""layers""")
			if "convolution" in name and "backbone" in name:
						a_						:						Dict 							= name.replace("""convolution"""  ,	"""conv""")
			if "layer" in name and "backbone" in name:
						a_						:						Optional[int] 							= name.replace("""layer"""  ,	"""layers""")
			if "backbone.bit.encoder.bit" in name:
						a_						:						Union[str, Any] 							= name.replace("""backbone.bit.encoder.bit"""  ,	"""backbone.bit""")
			if "embedder.conv" in name:
						a_						:						List[str] 							= name.replace("""embedder.conv"""  ,	"""embedder.convolution""")
			if "backbone.bit.encoder.stem.norm" in name:
						a_						:						str 							= name.replace("""backbone.bit.encoder.stem.norm"""  ,	"""backbone.bit.embedder.norm""")
			return name


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			for i in range(config.num_hidden_layers):
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						a_						:						Optional[int] 							= state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight''')
						a_						:						str 							= state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias''')
						# next, add query, keys and values (in that order) to the state dict
						a_						:						Any 							= in_proj_weight[: config.hidden_size, :]
						a_						:						List[str] 							= in_proj_bias[: config.hidden_size]
						a_						:						List[str] 							= in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						a_						:						str 							= in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						a_						:						Any 							= in_proj_weight[
						    -config.hidden_size :, :
						]
						a_						:						List[str] 							= in_proj_bias[-config.hidden_size :]


def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						Dict 							= """http://images.cocodataset.org/val2017/000000039769.jpg"""
			a_						:						Union[str, Any] 							= Image.open(requests.get(a__  ,	stream=a__).raw)
			return im


@torch.no_grad()
def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_							,					a_						:						str 							= get_dpt_config(a__)
			# load original state_dict from URL
			# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
			a_						:						str 							= torch.load(a__  ,	map_location="""cpu""")
			# remove certain keys
			remove_ignore_keys_(a__)
			# rename keys
			for key in state_dict.copy().keys():
						a_						:						List[str] 							= state_dict.pop(a__)
						a_						:						str 							= val
			# read in qkv matrices
			read_in_q_k_v(a__  ,	a__)

			# load HuggingFace model
			a_						:						Any 							= DPTForSemanticSegmentation(a__) if """ade""" in checkpoint_url else DPTForDepthEstimation(a__)
			model.load_state_dict(a__)
			model.eval()

			# Check outputs on an image
			a_						:						Any 							= 4_8_0 if """ade""" in checkpoint_url else 3_8_4
			a_						:						Any 							= DPTImageProcessor(size=a__)

			a_						:						Dict 							= prepare_img()
			a_						:						int 							= image_processor(a__  ,	return_tensors="""pt""")

			# forward pass
			a_						:						Optional[Any] 							= model(**a__).logits if """ade""" in checkpoint_url else model(**a__).predicted_depth

			if show_prediction:
						a_						:						List[str] 							= (
						    torch.nn.functional.interpolate(
						        outputs.unsqueeze(1)  ,	size=(image.size[1], image.size[0])  ,	mode="""bicubic"""  ,	align_corners=a__  ,	)
						    .squeeze()
						    .cpu()
						    .numpy()
						)

						Image.fromarray((prediction / prediction.max()) * 2_5_5).show()

			if pytorch_dump_folder_path is not None:
						Path(a__).mkdir(exist_ok=a__)
						print(f'''Saving model to {pytorch_dump_folder_path}''')
						model.save_pretrained(a__)
						print(f'''Saving image processor to {pytorch_dump_folder_path}''')
						image_processor.save_pretrained(a__)

			if push_to_hub:
						model.push_to_hub("""ybelkada/dpt-hybrid-midas""")
						image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas""")


if __name__ == "__main__":
	__snake_case				:      str   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--checkpoint_url""",
	    default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
	    type=str,
	    help="""URL of the original DPT checkpoint you'd like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""",
	    default=None,
	    type=str,
	    required=False,
	    help="""Path to the output PyTorch model directory.""",
	)
	parser.add_argument(
	    """--push_to_hub""",
	    action="""store_true""",
	)
	parser.add_argument(
	    """--model_name""",
	    default="""dpt-large""",
	    type=str,
	    help="""Name of the model, in case you're pushing to the hub.""",
	)
	parser.add_argument(
	    """--show_prediction""",
	    action="""store_true""",
	)

	__snake_case				:      str   		=			parser.parse_args()
	convert_dpt_checkpoint(
	    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
	)


